



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM

@require_tf
@require_sentencepiece
@require_tokenizers
class  snake_case__(unittest.TestCase     ):

					"""simple docstring"""






					@slow
					def 					snake_case	(				self   :      List[Any]	):
									lowercase__	:       str						=	TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"	)
									lowercase__	:       Union[str, Any]						=	AutoTokenizer.from_pretrained("google/mt5-small"	)

									lowercase__	:       int						=	tokenizer("Hello there" ,  return_tensors="tf"	).input_ids
									lowercase__	:       int						=	tokenizer("Hi I am" ,  return_tensors="tf"	).input_ids

									lowercase__	:       Dict						=	model(lowerCAmelCase__ ,  labels=lowerCAmelCase__	).loss
									lowercase__	:       List[str]						=	-tf.math.reduce_mean(lowerCAmelCase__	).numpy()

									lowercase__	:       Any						=	-21.228_168
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 2E-4	)





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				len(lowercase				)
				__lowercase					     =				len(lowercase				)
				__lowercase					     =				[[False for _ in range(m + 1				)] for _ in range(n + 1				)]
				__lowercase					     =				True
				for i in range(lowercase				):
								for j in range(m + 1				):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				__lowercase					     =				True
																if a[i].islower():
																				__lowercase					     =				True
				return dp[n][m]


if __name__ == "__main__":
		import doctest

		doctest.testmod()